The measurement and analysis of human movement has many applications including the enhancement of athletic performance, the rehabilitation from injury, and the diagnosis of neurological disorders.
Bodily movements can be measured using a wide variety of techniques and types of sensors. Wearable inertial sensors—which include accelerometers and gyroscopes—enjoy the advantage that they are simple, unobtrusive, and self-contained. They are thus very suitable for continuously recording data over an extended period of time while the the subject performs normal activities of daily life at home. A typical inertial measurement unit (IMU), for example, is a compact wearable device that contains a triaxial accelerometer and/or triaxial gyroscope for measuring movement at a particular position on the body where it is worn. Accelerometers measure the translational acceleration in addition to gravity, and gyroscopes measure angular velocities. An IMU may also contain other types of sensors such as magnetometers. Existing techniques track the movement of a multi-segment limb by placing an IMU at each segment of the limb, e.g., on the shoulder, upper arm, and forearm. Inertial data from the multiple sensors are collected over a period of time, transferred to a computer, and used to estimate the movement of the segments (i.e., their orientations and positions) as a function of time.
Various techniques are known for estimating an orientation of human body segment from inertial data. Traditionally, the orientation of a segment is estimated by integrating the angular velocities measured by gyroscopes, and position is obtained by double integration of the translational acceleration measured by accelerometers. A significant problem with integration, however, is that inaccuracies inherent in the measurements quickly accumulate in the integrated estimation, resulting in an unacceptable levels of integration drift in just a few minutes.
One approach to reducing integration drift is to fuse the gyroscope data with complementary data from magnetometers or other sensors. Increasing the number of sensors helps improve accuracy, but it increases the expense and complexity of the inertial measurement units. It remains a challenge to accurately estimate the state of multiple joints of a limb over extended periods of time with just a few sensors. All known techniques, for example, require a separate inertial sensor unit positioned at each distinct segment of the limb.
A common tracking approach involves applying the Kalman filter for fusing different sensor measurements. However, the linear Kalman filter results in tracking errors when used in a highly nonlinear dynamics. Also, the extended Kalman filter (EKF) is based upon linearizing the state transition and observation matrix with a first-order Taylor expansion, resulting in poor performance when the dynamics are highly nonlinear. The EKF also requires Jacobian matrices and inverse matrix calculation. It also has stability and convergence problems.
In view of the above, there remains a need for improved techniques for accurately tracking the motion of multi-segment limbs for extended periods of time with a small number of sensors.